"""
    Performance Profiling Module

Comprehensive performance profiling and monitoring tools for GSICoreAnalysis.jl including:
- Detailed timing measurements for all major operations
- Memory usage profiling and leak detection
- Performance bottleneck identification
- Comparative benchmarking and regression testing
- Performance dashboard and reporting
- Automatic performance optimization suggestions

# Usage

```julia
using GSICoreAnalysis.Performance.Profiling

# Start performance profiling
profiler = PerformanceProfiler(
    enable_timing = true,
    enable_memory_tracking = true,
    enable_bottleneck_detection = true
)

# Profile a function
@profile profiler "operation_name" begin
    result = expensive_operation(data)
end

# Generate performance report
report = generate_performance_report(profiler)
save_performance_report(report, "performance_analysis.html")

# Benchmark operations
benchmark_results = comprehensive_benchmark(test_configurations)
identify_regressions(benchmark_results, baseline_results)
```
"""
module Profiling

using BenchmarkTools
using Profile
using Statistics
using Printf
using Dates
using LinearAlgebra
using Base.Threads
using DataStructures

# Export main types and functions
export PerformanceProfiler, BenchmarkSuite, PerformanceReport
export ProfileSession, TimingResult, MemoryProfile, BottleneckAnalysis
export enable_profiling, disable_profiling, @profile
export benchmark_operation, comprehensive_benchmark
export generate_performance_report, save_performance_report
export identify_bottlenecks, detect_performance_regressions
export performance_dashboard, monitor_real_time_performance
export optimize_performance_configuration, suggest_optimizations
export compare_performance, validate_performance_improvements

"""
    PerformanceProfiler

Main profiling system for tracking performance metrics.
"""
mutable struct PerformanceProfiler
    name::String
    enable_timing::Bool
    enable_memory_tracking::Bool
    enable_bottleneck_detection::Bool
    enable_thread_analysis::Bool
    is_active::Bool
    session_start::DateTime
    timing_results::Dict{String, Vector{Float64}}
    memory_profiles::Dict{String, Vector{NamedTuple}}
    bottlenecks::Vector{NamedTuple}
    thread_utilization::Vector{Float64}
    operation_counts::Dict{String, Int}
    call_hierarchy::Dict{String, Vector{String}}
    
    function PerformanceProfiler(name::String = "default";
        enable_timing = true,
        enable_memory_tracking = true,
        enable_bottleneck_detection = true,
        enable_thread_analysis = nthreads() > 1
    )
        new(name, enable_timing, enable_memory_tracking, enable_bottleneck_detection,
            enable_thread_analysis, false, now(), 
            Dict{String, Vector{Float64}}(),
            Dict{String, Vector{NamedTuple}}(),
            NamedTuple[],
            Float64[],
            Dict{String, Int}(),
            Dict{String, Vector{String}}())
    end
end

"""
    BenchmarkSuite

Collection of benchmarks for comprehensive performance testing.
"""
struct BenchmarkSuite
    name::String
    benchmarks::Dict{String, Function}
    configurations::Dict{String, Any}
    baseline_results::Union{Nothing, Dict{String, BenchmarkTools.Trial}}
    
    function BenchmarkSuite(name::String, 
                           benchmarks::Dict{String, Function} = Dict{String, Function}(),
                           configurations::Dict{String, Any} = Dict{String, Any}())
        new(name, benchmarks, configurations, nothing)
    end
end

"""
    PerformanceReport

Comprehensive performance analysis report.
"""
struct PerformanceReport
    profiler_name::String
    generation_time::DateTime
    session_duration::Float64
    timing_analysis::Dict{String, NamedTuple}
    memory_analysis::Dict{String, NamedTuple}
    bottleneck_analysis::Vector{NamedTuple}
    thread_analysis::NamedTuple
    recommendations::Vector{String}
    performance_score::Float64
    
    function PerformanceReport(profiler::PerformanceProfiler)
        timing_analysis = analyze_timing_results(profiler.timing_results)
        memory_analysis = analyze_memory_profiles(profiler.memory_profiles)
        bottleneck_analysis = analyze_bottlenecks(profiler.bottlenecks)
        thread_analysis = analyze_thread_utilization(profiler.thread_utilization)
        recommendations = generate_optimization_recommendations(profiler)
        performance_score = calculate_performance_score(profiler)
        
        new(profiler.name, now(), 
            (now() - profiler.session_start).value / 1000.0,
            timing_analysis, memory_analysis, bottleneck_analysis,
            thread_analysis, recommendations, performance_score)
    end
end

"""
    ProfileSession

Individual profiling session data.
"""
struct ProfileSession
    operation_name::String
    start_time::DateTime
    end_time::DateTime
    execution_time::Float64
    memory_allocated::Int64
    memory_freed::Int64
    gc_time::Float64
    thread_id::Int
    call_stack::Vector{String}
end

"""
    TimingResult

Detailed timing measurement result.
"""
struct TimingResult
    operation_name::String
    execution_time::Float64
    cpu_time::Float64
    wall_time::Float64
    samples::Int
    overhead::Float64
end

"""
    MemoryProfile

Memory usage profile for an operation.
"""
struct MemoryProfile
    operation_name::String
    initial_memory::Int64
    peak_memory::Int64
    final_memory::Int64
    allocations::Int64
    deallocations::Int64
    gc_collections::Int
    memory_efficiency::Float64
end

"""
    BottleneckAnalysis

Analysis of performance bottlenecks.
"""
struct BottleneckAnalysis
    operation_name::String
    bottleneck_type::Symbol  # :cpu, :memory, :io, :synchronization
    severity::Float64  # 0.0 to 1.0
    description::String
    suggested_fix::String
    impact_estimate::Float64
end

# Global profiler instance
const GLOBAL_PROFILER = Ref{Union{Nothing, PerformanceProfiler}}(nothing)

"""
    enable_profiling(profiler::PerformanceProfiler)

Enable performance profiling with the given profiler.
"""
function enable_profiling(profiler::PerformanceProfiler)
    profiler.is_active = true
    profiler.session_start = now()
    GLOBAL_PROFILER[] = profiler
    
    @info "Performance profiling enabled" profiler=profiler.name timing=profiler.enable_timing memory=profiler.enable_memory_tracking
    
    return profiler
end

"""
    disable_profiling(profiler::PerformanceProfiler)

Disable performance profiling.
"""
function disable_profiling(profiler::PerformanceProfiler)
    profiler.is_active = false
    GLOBAL_PROFILER[] = nothing
    
    @info "Performance profiling disabled" profiler=profiler.name session_duration=(now() - profiler.session_start).value / 1000.0
    
    return profiler
end

"""
    @profile profiler operation_name block

Macro for profiling a block of code.
"""
macro profile(profiler, operation_name, block)
    quote
        local prof = $(esc(profiler))
        local op_name = string($(esc(operation_name)))
        
        if prof.is_active
            profile_operation(prof, op_name) do
                $(esc(block))
            end
        else
            $(esc(block))
        end
    end
end

"""
    profile_operation(func::Function, profiler::PerformanceProfiler, operation_name::String)

Profile a function execution.
"""
function profile_operation(func::Function, profiler::PerformanceProfiler, operation_name::String)
    if !profiler.is_active
        return func()
    end
    
    # Initialize tracking
    start_time = now()
    initial_gc_stats = Base.gc_num()
    thread_id = threadid()
    
    # Memory tracking
    if profiler.enable_memory_tracking
        initial_memory = get_current_memory_usage()
    end
    
    # Execute function with timing
    start_cpu_time = time()
    result = func()
    end_cpu_time = time()
    
    end_time = now()
    final_gc_stats = Base.gc_num()
    
    # Calculate metrics
    execution_time = (end_time - start_time).value / 1000.0
    cpu_time = end_cpu_time - start_cpu_time
    
    # Memory metrics
    allocated = final_gc_stats.allocd - initial_gc_stats.allocd
    freed = final_gc_stats.freed - initial_gc_stats.freed
    gc_time = final_gc_stats.total_time - initial_gc_stats.total_time
    
    # Store timing results
    if profiler.enable_timing
        if !haskey(profiler.timing_results, operation_name)
            profiler.timing_results[operation_name] = Float64[]
        end
        push!(profiler.timing_results[operation_name], execution_time)
    end
    
    # Store memory profile
    if profiler.enable_memory_tracking
        memory_profile = (
            operation = operation_name,
            execution_time = execution_time,
            allocated_bytes = allocated,
            freed_bytes = freed,
            net_allocation = allocated - freed,
            gc_time_ms = gc_time,
            thread_id = thread_id
        )
        
        if !haskey(profiler.memory_profiles, operation_name)
            profiler.memory_profiles[operation_name] = NamedTuple[]
        end
        push!(profiler.memory_profiles[operation_name], memory_profile)
    end
    
    # Update operation count
    profiler.operation_counts[operation_name] = get(profiler.operation_counts, operation_name, 0) + 1
    
    # Bottleneck detection
    if profiler.enable_bottleneck_detection
        detect_bottleneck(profiler, operation_name, execution_time, allocated, gc_time)
    end
    
    return result
end

"""
    get_current_memory_usage()

Get current memory usage estimate.
"""
function get_current_memory_usage()
    # This is an approximation since Julia doesn't provide exact current usage
    gc_stats = Base.gc_num()
    return gc_stats.allocd - gc_stats.freed
end

"""
    detect_bottleneck(profiler::PerformanceProfiler, operation_name::String,
                     execution_time::Float64, memory_allocated::Int64, gc_time::Float64)

Detect performance bottlenecks in operations.
"""
function detect_bottleneck(profiler::PerformanceProfiler, operation_name::String,
                          execution_time::Float64, memory_allocated::Int64, gc_time::Float64)
    # Define bottleneck thresholds
    slow_execution_threshold = 1.0  # seconds
    high_memory_threshold = 100 * 1024 * 1024  # 100MB
    high_gc_threshold = 0.1  # 100ms
    
    bottlenecks_found = NamedTuple[]
    
    # CPU bottleneck detection
    if execution_time > slow_execution_threshold
        bottleneck = (
            operation = operation_name,
            type = :cpu,
            severity = min(1.0, execution_time / slow_execution_threshold),
            description = "Slow execution time: $(round(execution_time, digits=3))s",
            suggested_fix = "Consider algorithmic optimization or parallelization",
            timestamp = now()
        )
        push!(bottlenecks_found, bottleneck)
    end
    
    # Memory bottleneck detection
    if memory_allocated > high_memory_threshold
        bottleneck = (
            operation = operation_name,
            type = :memory,
            severity = min(1.0, memory_allocated / high_memory_threshold),
            description = "High memory allocation: $(round(memory_allocated / 1024^2, digits=1))MB",
            suggested_fix = "Consider memory pooling or in-place operations",
            timestamp = now()
        )
        push!(bottlenecks_found, bottleneck)
    end
    
    # GC bottleneck detection
    if gc_time > high_gc_threshold
        bottleneck = (
            operation = operation_name,
            type = :gc,
            severity = min(1.0, gc_time / high_gc_threshold),
            description = "High GC time: $(round(gc_time * 1000, digits=1))ms",
            suggested_fix = "Reduce allocations or use object pooling",
            timestamp = now()
        )
        push!(bottlenecks_found, bottleneck)
    end
    
    # Store bottlenecks
    append!(profiler.bottlenecks, bottlenecks_found)
end

"""
    benchmark_operation(operation::Function, name::String, args...; 
                       samples=5, seconds=10)

Benchmark a specific operation.
"""
function benchmark_operation(operation::Function, name::String, args...; 
                            samples=5, seconds=10)
    @info "Benchmarking operation: $name"
    
    # Warm-up
    operation(args...)
    
    # Run benchmark
    benchmark = @benchmark $operation($args...) samples=samples seconds=seconds
    
    timing_result = TimingResult(
        name,
        median(benchmark.times) / 1e9,  # Convert to seconds
        minimum(benchmark.times) / 1e9,
        maximum(benchmark.times) / 1e9,
        length(benchmark.times),
        BenchmarkTools.overhead()
    )
    
    @info "Benchmark completed" operation=name median_time="$(round(timing_result.execution_time * 1000, digits=2))ms"
    
    return timing_result, benchmark
end

"""
    comprehensive_benchmark(configurations::Vector{Dict{String, Any}})

Run comprehensive benchmarks across different configurations.
"""
function comprehensive_benchmark(configurations::Vector{Dict{String, Any}})
    @info "Starting comprehensive benchmark" n_configs=length(configurations)
    
    results = Dict{String, Any}()
    
    for (i, config) in enumerate(configurations)
        config_name = get(config, "name", "config_$i")
        @info "Running benchmark configuration: $config_name"
        
        config_results = Dict{String, Any}()
        
        # Benchmark basic operations
        config_results["vector_operations"] = benchmark_vector_operations(config)
        config_results["matrix_operations"] = benchmark_matrix_operations(config)
        config_results["memory_operations"] = benchmark_memory_operations(config)
        config_results["parallel_operations"] = benchmark_parallel_operations(config)
        
        results[config_name] = config_results
    end
    
    @info "Comprehensive benchmark completed"
    
    return results
end

"""
    benchmark_vector_operations(config::Dict{String, Any})

Benchmark vector operations.
"""
function benchmark_vector_operations(config::Dict{String, Any})
    n = get(config, "vector_size", 10000)
    
    # Create test data
    a = randn(n)
    b = randn(n)
    
    results = Dict{String, TimingResult}()
    
    # Vector addition
    timing, _ = benchmark_operation(+, "vector_add", a, b)
    results["add"] = timing
    
    # Vector dot product  
    timing, _ = benchmark_operation(dot, "vector_dot", a, b)
    results["dot"] = timing
    
    # Vector norm
    timing, _ = benchmark_operation(norm, "vector_norm", a)
    results["norm"] = timing
    
    return results
end

"""
    benchmark_matrix_operations(config::Dict{String, Any})

Benchmark matrix operations.
"""
function benchmark_matrix_operations(config::Dict{String, Any})
    n = get(config, "matrix_size", 100)
    
    # Create test matrices
    A = randn(n, n)
    B = randn(n, n)
    
    results = Dict{String, TimingResult}()
    
    # Matrix multiplication
    timing, _ = benchmark_operation(*, "matrix_mult", A, B)
    results["mult"] = timing
    
    # Matrix transpose
    timing, _ = benchmark_operation(transpose, "matrix_transpose", A)
    results["transpose"] = timing
    
    # Matrix inverse (for smaller matrices)
    if n <= 50
        timing, _ = benchmark_operation(inv, "matrix_inverse", A)
        results["inverse"] = timing
    end
    
    return results
end

"""
    benchmark_memory_operations(config::Dict{String, Any})

Benchmark memory operations.
"""
function benchmark_memory_operations(config::Dict{String, Any})
    n = get(config, "array_size", 100000)
    
    results = Dict{String, TimingResult}()
    
    # Array allocation
    timing, _ = benchmark_operation(() -> Vector{Float64}(undef, n), "array_alloc")
    results["allocation"] = timing
    
    # Array copy
    test_array = randn(n)
    timing, _ = benchmark_operation(copy, "array_copy", test_array)
    results["copy"] = timing
    
    # Array fill
    target_array = Vector{Float64}(undef, n)
    timing, _ = benchmark_operation(fill!, "array_fill", target_array, 1.0)
    results["fill"] = timing
    
    return results
end

"""
    benchmark_parallel_operations(config::Dict{String, Any})

Benchmark parallel operations.
"""
function benchmark_parallel_operations(config::Dict{String, Any})
    n = get(config, "parallel_size", 10000)
    data = randn(n)
    
    results = Dict{String, TimingResult}()
    
    # Sequential sum
    timing, _ = benchmark_operation(sum, "sequential_sum", data)
    results["sequential_sum"] = timing
    
    # Parallel sum (if multiple threads available)
    if nthreads() > 1
        parallel_sum = (arr) -> begin
            partial_sums = zeros(nthreads())
            @threads for tid in 1:nthreads()
                thread_start = ((tid - 1) * length(arr)) ÷ nthreads() + 1
                thread_end = (tid * length(arr)) ÷ nthreads()
                partial_sums[tid] = sum(@view arr[thread_start:thread_end])
            end
            return sum(partial_sums)
        end
        
        timing, _ = benchmark_operation(parallel_sum, "parallel_sum", data)
        results["parallel_sum"] = timing
    end
    
    return results
end

"""
    analyze_timing_results(timing_results::Dict{String, Vector{Float64}})

Analyze timing results for operations.
"""
function analyze_timing_results(timing_results::Dict{String, Vector{Float64}})
    analysis = Dict{String, NamedTuple}()
    
    for (operation, times) in timing_results
        if !isempty(times)
            analysis[operation] = (
                count = length(times),
                mean_time = mean(times),
                median_time = median(times),
                min_time = minimum(times),
                max_time = maximum(times),
                std_time = std(times),
                total_time = sum(times),
                throughput = length(times) / sum(times)  # operations per second
            )
        end
    end
    
    return analysis
end

"""
    analyze_memory_profiles(memory_profiles::Dict{String, Vector{NamedTuple}})

Analyze memory usage profiles.
"""
function analyze_memory_profiles(memory_profiles::Dict{String, Vector{NamedTuple}})
    analysis = Dict{String, NamedTuple}()
    
    for (operation, profiles) in memory_profiles
        if !isempty(profiles)
            allocations = [p.allocated_bytes for p in profiles]
            deallocations = [p.freed_bytes for p in profiles]
            net_allocations = [p.net_allocation for p in profiles]
            gc_times = [p.gc_time_ms for p in profiles]
            
            analysis[operation] = (
                count = length(profiles),
                mean_allocation = mean(allocations),
                total_allocation = sum(allocations),
                mean_deallocation = mean(deallocations),
                mean_net_allocation = mean(net_allocations),
                total_net_allocation = sum(net_allocations),
                mean_gc_time = mean(gc_times),
                total_gc_time = sum(gc_times),
                memory_efficiency = sum(deallocations) / max(1, sum(allocations))
            )
        end
    end
    
    return analysis
end

"""
    analyze_bottlenecks(bottlenecks::Vector{NamedTuple})

Analyze detected bottlenecks.
"""
function analyze_bottlenecks(bottlenecks::Vector{NamedTuple})
    if isempty(bottlenecks)
        return NamedTuple[]
    end
    
    # Group bottlenecks by type
    by_type = Dict{Symbol, Vector{NamedTuple}}()
    for bottleneck in bottlenecks
        bt_type = bottleneck.type
        if !haskey(by_type, bt_type)
            by_type[bt_type] = NamedTuple[]
        end
        push!(by_type[bt_type], bottleneck)
    end
    
    # Analyze each type
    analysis = NamedTuple[]
    for (bt_type, bt_list) in by_type
        severities = [bt.severity for bt in bt_list]
        
        type_analysis = (
            type = bt_type,
            count = length(bt_list),
            mean_severity = mean(severities),
            max_severity = maximum(severities),
            most_affected_operations = unique([bt.operation for bt in bt_list]),
            recommendations = unique([bt.suggested_fix for bt in bt_list])
        )
        
        push!(analysis, type_analysis)
    end
    
    return analysis
end

"""
    analyze_thread_utilization(thread_utilization::Vector{Float64})

Analyze thread utilization patterns.
"""
function analyze_thread_utilization(thread_utilization::Vector{Float64})
    if isempty(thread_utilization)
        return (
            available_threads = nthreads(),
            utilization_data = false,
            mean_utilization = 0.0,
            peak_utilization = 0.0,
            efficiency_score = 0.0
        )
    end
    
    return (
        available_threads = nthreads(),
        utilization_data = true,
        mean_utilization = mean(thread_utilization),
        peak_utilization = maximum(thread_utilization),
        min_utilization = minimum(thread_utilization),
        efficiency_score = mean(thread_utilization) / nthreads(),
        samples = length(thread_utilization)
    )
end

"""
    generate_optimization_recommendations(profiler::PerformanceProfiler)

Generate optimization recommendations based on profiling results.
"""
function generate_optimization_recommendations(profiler::PerformanceProfiler)
    recommendations = String[]
    
    # Analyze timing results
    for (operation, times) in profiler.timing_results
        if !isempty(times)
            mean_time = mean(times)
            if mean_time > 1.0  # Operations taking more than 1 second
                push!(recommendations, "Consider optimizing '$operation' - average execution time: $(round(mean_time, digits=2))s")
            end
        end
    end
    
    # Analyze memory usage
    for (operation, profiles) in profiler.memory_profiles
        if !isempty(profiles)
            total_allocation = sum(p.net_allocation for p in profiles)
            if total_allocation > 100 * 1024 * 1024  # More than 100MB
                push!(recommendations, "High memory usage in '$operation' - consider memory optimization techniques")
            end
        end
    end
    
    # Analyze bottlenecks
    bottleneck_operations = Set{String}()
    for bottleneck in profiler.bottlenecks
        push!(bottleneck_operations, bottleneck.operation)
    end
    
    if !isempty(bottleneck_operations)
        push!(recommendations, "Focus optimization efforts on: $(join(bottleneck_operations, ", "))")
    end
    
    # Thread utilization
    if profiler.enable_thread_analysis && nthreads() > 1
        push!(recommendations, "Consider parallel processing for CPU-intensive operations")
    end
    
    # General recommendations
    if isempty(recommendations)
        push!(recommendations, "Performance profile looks good - no major optimization opportunities identified")
    end
    
    return recommendations
end

"""
    calculate_performance_score(profiler::PerformanceProfiler)

Calculate overall performance score.
"""
function calculate_performance_score(profiler::PerformanceProfiler)
    score = 100.0  # Start with perfect score
    
    # Penalize for slow operations
    for (operation, times) in profiler.timing_results
        if !isempty(times)
            mean_time = mean(times)
            if mean_time > 1.0
                score -= min(20, mean_time * 5)  # Penalize slow operations
            end
        end
    end
    
    # Penalize for memory inefficiency
    for (operation, profiles) in profiler.memory_profiles
        if !isempty(profiles)
            avg_efficiency = mean(p.freed_bytes / max(1, p.allocated_bytes) for p in profiles)
            if avg_efficiency < 0.5  # Less than 50% memory efficiency
                score -= 15
            end
        end
    end
    
    # Penalize for bottlenecks
    high_severity_bottlenecks = count(bt -> bt.severity > 0.7, profiler.bottlenecks)
    score -= high_severity_bottlenecks * 10
    
    return max(0.0, score)
end

"""
    generate_performance_report(profiler::PerformanceProfiler)

Generate comprehensive performance report.
"""
function generate_performance_report(profiler::PerformanceProfiler)
    return PerformanceReport(profiler)
end

"""
    save_performance_report(report::PerformanceReport, filepath::String)

Save performance report to file.
"""
function save_performance_report(report::PerformanceReport, filepath::String)
    if endswith(filepath, ".html")
        save_html_report(report, filepath)
    else
        save_text_report(report, filepath)
    end
end

"""
    save_text_report(report::PerformanceReport, filepath::String)

Save performance report as text file.
"""
function save_text_report(report::PerformanceReport, filepath::String)
    open(filepath, "w") do io
        println(io, "Performance Analysis Report")
        println(io, "===========================")
        println(io, "Profiler: $(report.profiler_name)")
        println(io, "Generated: $(report.generation_time)")
        println(io, "Session Duration: $(round(report.session_duration, digits=2)) seconds")
        println(io, "Performance Score: $(round(report.performance_score, digits=1))/100")
        println(io)
        
        # Timing Analysis
        println(io, "Timing Analysis:")
        println(io, "----------------")
        for (operation, analysis) in report.timing_analysis
            println(io, "  $operation:")
            println(io, "    Count: $(analysis.count)")
            println(io, "    Mean Time: $(round(analysis.mean_time * 1000, digits=2)) ms")
            println(io, "    Median Time: $(round(analysis.median_time * 1000, digits=2)) ms")
            println(io, "    Total Time: $(round(analysis.total_time, digits=2)) s")
            println(io, "    Throughput: $(round(analysis.throughput, digits=1)) ops/s")
            println(io)
        end
        
        # Memory Analysis
        println(io, "Memory Analysis:")
        println(io, "----------------")
        for (operation, analysis) in report.memory_analysis
            println(io, "  $operation:")
            println(io, "    Mean Allocation: $(round(analysis.mean_allocation / 1024^2, digits=2)) MB")
            println(io, "    Total Allocation: $(round(analysis.total_allocation / 1024^2, digits=2)) MB")
            println(io, "    Memory Efficiency: $(round(analysis.memory_efficiency * 100, digits=1))%")
            println(io)
        end
        
        # Recommendations
        println(io, "Optimization Recommendations:")
        println(io, "-----------------------------")
        for (i, rec) in enumerate(report.recommendations)
            println(io, "  $i. $rec")
        end
    end
    
    @info "Performance report saved" filepath=filepath
end

"""
    save_html_report(report::PerformanceReport, filepath::String)

Save performance report as HTML file with charts.
"""
function save_html_report(report::PerformanceReport, filepath::String)
    # This would generate a more sophisticated HTML report with charts
    # For now, convert to text format
    save_text_report(report, replace(filepath, ".html" => ".txt"))
    @info "HTML report functionality not yet implemented, saved as text file"
end

"""
    identify_bottlenecks(profiler::PerformanceProfiler)

Identify and return current performance bottlenecks.
"""
function identify_bottlenecks(profiler::PerformanceProfiler)
    bottlenecks = BottleneckAnalysis[]
    
    # Analyze timing bottlenecks
    for (operation, times) in profiler.timing_results
        if !isempty(times)
            mean_time = mean(times)
            if mean_time > 0.5  # 500ms threshold
                severity = min(1.0, mean_time / 2.0)  # Scale to [0,1]
                
                analysis = BottleneckAnalysis(
                    operation,
                    :cpu,
                    severity,
                    "Slow execution: $(round(mean_time * 1000, digits=1))ms average",
                    "Consider algorithmic optimization or parallelization",
                    severity * mean_time
                )
                
                push!(bottlenecks, analysis)
            end
        end
    end
    
    # Sort by impact estimate
    sort!(bottlenecks, by = ba -> ba.impact_estimate, rev = true)
    
    return bottlenecks
end

"""
    detect_performance_regressions(current_results::Dict, baseline_results::Dict, 
                                  threshold::Float64 = 0.1)

Detect performance regressions compared to baseline.
"""
function detect_performance_regressions(current_results::Dict, baseline_results::Dict, 
                                       threshold::Float64 = 0.1)
    regressions = NamedTuple[]
    
    for (operation, current_stats) in current_results
        if haskey(baseline_results, operation)
            baseline_stats = baseline_results[operation]
            
            # Compare execution times
            if haskey(current_stats, :mean_time) && haskey(baseline_stats, :mean_time)
                current_time = current_stats.mean_time
                baseline_time = baseline_stats.mean_time
                
                regression_factor = (current_time - baseline_time) / baseline_time
                
                if regression_factor > threshold
                    regression = (
                        operation = operation,
                        metric = :execution_time,
                        current_value = current_time,
                        baseline_value = baseline_time,
                        regression_factor = regression_factor,
                        severity = min(1.0, regression_factor / 0.5)
                    )
                    
                    push!(regressions, regression)
                end
            end
        end
    end
    
    return regressions
end

"""
    compare_performance(results1::Dict, results2::Dict, name1::String = "Config 1", 
                       name2::String = "Config 2")

Compare performance between two configurations.
"""
function compare_performance(results1::Dict, results2::Dict, name1::String = "Config 1", 
                            name2::String = "Config 2")
    comparison = Dict{String, NamedTuple}()
    
    all_operations = Set(keys(results1)) ∪ Set(keys(results2))
    
    for operation in all_operations
        has_1 = haskey(results1, operation)
        has_2 = haskey(results2, operation)
        
        if has_1 && has_2
            stats1 = results1[operation]
            stats2 = results2[operation]
            
            if haskey(stats1, :mean_time) && haskey(stats2, :mean_time)
                time1 = stats1.mean_time
                time2 = stats2.mean_time
                speedup = time1 / time2
                
                comparison[operation] = (
                    config1_time = time1,
                    config2_time = time2,
                    speedup = speedup,
                    winner = speedup > 1.0 ? name2 : name1,
                    improvement = abs(1.0 - speedup)
                )
            end
        elseif has_1
            comparison[operation] = (
                available_in = name1,
                config1_time = results1[operation][:mean_time],
                config2_time = missing,
                speedup = missing,
                winner = name1,
                improvement = missing
            )
        else
            comparison[operation] = (
                available_in = name2,
                config1_time = missing,
                config2_time = results2[operation][:mean_time],
                speedup = missing,
                winner = name2,
                improvement = missing
            )
        end
    end
    
    return comparison
end

"""
    validate_performance_improvements(before_results::Dict, after_results::Dict)

Validate that performance improvements are significant.
"""
function validate_performance_improvements(before_results::Dict, after_results::Dict)
    improvements = NamedTuple[]
    
    for operation in keys(before_results) ∩ keys(after_results)
        before_stats = before_results[operation]
        after_stats = after_results[operation]
        
        if haskey(before_stats, :mean_time) && haskey(after_stats, :mean_time)
            before_time = before_stats.mean_time
            after_time = after_stats.mean_time
            
            if before_time > after_time
                improvement_factor = (before_time - after_time) / before_time
                
                improvement = (
                    operation = operation,
                    before_time = before_time,
                    after_time = after_time,
                    improvement_factor = improvement_factor,
                    speedup = before_time / after_time,
                    is_significant = improvement_factor > 0.05  # 5% improvement threshold
                )
                
                push!(improvements, improvement)
            end
        end
    end
    
    # Sort by improvement factor
    sort!(improvements, by = imp -> imp.improvement_factor, rev = true)
    
    return improvements
end

end # module Profiling